A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method improves estimation of complex models from conditional moment restrictions.
New method tightens sub-Gaussian concentration inequalities.
Spectral features of the empirical moment matrix constitute a resourceful tool for unveiling properties of a cloud of points, among which, density, support and latent structures. It is already well known that the empirical moment matrix encodes a great deal of subtle attributes of the underlying measure. Starting from …
The paper provides a new uniform tail bound for empirical processes.
A new method of moments estimator goes beyond data reweighting.
New bounds on machine learning model generalization error moments.
New stability framework relaxes boundedness assumptions for generalization bounds.
New SGMM algorithm for efficient estimation of moment restriction models.
A new method calculates fractional moments using the moment-generating function.
A new method for generating samples without training, using smoothed score matching.
For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time hori…
Uniform deviation bounds limit the difference between a model's expected loss and its loss on an empirical sample uniformly for all models in a learning problem. As such, they are a critical component to empirical risk minimization. In this paper, we provide a novel framework to obtain uniform deviation bounds for loss…
Bayesian framework uses AI-generated data to improve parameter estimation.
GANs learn distributions by matching low-degree moments.
This paper identifies and bounds ICE central moments using PO marginal central moments.
We consider two stage estimation with a non-parametric first stage and a generalized method of moments second stage, in a simpler setting than (Chernozhukov et al. 2016). We give an alternative proof of the theorem given in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample splitting and -…
New margin-based learning guarantees improve generalization bounds.
This article investigates parameter estimation of affine term structure models by means of the generalized method of moments. Exact moments of the affine latent process as well as of the yields are obtained by using results derived for p-polynomial processes. Then the generalized method of moments, combined with Quasi-…
New bounds for neural networks without loss boundedness assumption.
New method generates realistic financial price paths with drawdowns.
We prove semi-empirical concentration inequalities for random variables which are given as possibly nonlinear functions of independent random variables. These inequalities describe concentration of random variable in terms of the data/distribution-dependent Efron-Stein (ES) estimate of its variance and they do not requ…
We propose -graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment -score reduces the influence of contamination and robustly measures the difference between the underlying correct expected weights of links and the specified generative m…
Proposes DWMD for better matching of hidden representations across domains.
The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed. We propose a practical private EM algorithm that overcomes this challenge using two innovations: (1) a novel moment perturbation formulation…
Many complex systems generate multifractal time series which are long-range cross-correlated. Numerous methods have been proposed to characterize the multifractal nature of these long-range cross correlations. However, several important issues about these methods are not well understood and most methods consider only o…
We improve bounds for stochastic processes, especially those with heavy tails.
The paper improves generalization bounds for domain adaptation.
Study moment maps coupled with convex functions to find critical points.
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments. While Adam is an adaptive lower-order moment based (of the stochastic gradient) method, we propose an extension namely, HAdam, which uses higher order moments of the stochastic gradient. Our …
This article proposes a new method for the estimation of the parameters of a simple linear regression model which accounts for the role of co-moments in non-Gaussian distributions being based on the minimization of a quartic loss function. Although the proposed method is very general, we examine its application to fina…
We consider a stochastic volatility model where the moment generating function of the logarithmic price is finite only on part of the real line. Using a new Tauberian result obtained in [1] and [2], we show that the knowledge of the moment generating function near its critical moment gives a sharp asymptotic expansion …
Generative adversarial networks are a novel method for statistical inference that have achieved much empirical success; however, the factors contributing to this success remain ill-understood. In this work, we attempt to analyze generative adversarial learning -- that is, statistical inference as the result of a game b…
Paper proposes robust risk measures for non-negative risks with partial information.
The paper analyzes the performance of empirical risk minimization for -norm linear regression.
Paper develops efficient DML estimators for multiway clustered data without cross-fitting.
Kurtosis is seen as a measure of the discrepancy between the observed data and a Gaussian distribution and is defined when the 4th moment is finite. In this work an empirical study is conducted to investigate the behaviour of the sample estimate of kurtosis with respect to sample size and the tail index when applied to…
New neural networks learn distribution functions using quantiles and moments.
The paper examines the tilted empirical risk's generalization and robustness under negative tilt.
The MEM method uses data-driven priors for linear inverse problems, proving convergence and estimating differences.
In this paper we will study the statistics of the unit geodesic flow normal to the boundary of a hyperbolic manifold with non-empty totally geodesic boundary. Viewing the time it takes this flow to hit the boundary as a random variable, we derive a formula for its moments in terms of the orthospectrum. The first moment…
New technique reduces imitation learning performance gap in finite samples.
The hidden tail of empirical distributions is analyzed using extreme value theory.
Efficient policy learning from observational data using weighted classification reductions.
We develop a scale-invariant truncated Lévy (STL) process to describe physical systems characterized by correlated stochastic variables. The STL process exhibits Lévy stability for the probability density, and hence shows scaling properties (as observed in empirical data); it has the advantage that all moments are fini…
New KCM tests improve specification testing via RKHS.
New statistical test for change-point detection using relative entropy.
In this paper, we consider generalized moment maps for Hamiltonian actions on -twisted generalized complex manifolds introduced by Lin and Tolman \cite{Lin}. The main purpose of this paper is to show convexity and connectedness properties for generalized moment maps. We study Hamiltonian torus actions on compact …
Deviation inequalities for stochastic approximation methods.